Preprocessing Noise Reduction For Assistive Listening System

Nurul, Md Yunus and Noor Aliff, Noor Affande and Roshahliza, M. Ramli and Noor, Ali O. Abid and Salina, Abdul Samad (2021) Preprocessing Noise Reduction For Assistive Listening System. In: IEEE 1st International Conference on Electronic and Electrical Engineering and Intelligent Systems (ICE3IS 2021) , 15-16 October 2021 , Online. pp. 114-119.. ISBN 9781665405461

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Abstract

This research aims to investigate the preprocessing stage of the noise cancellation system to tackle the problem of hearing loss in the industry. A hearing aid may help one to hear sounds, but it will not filter or eliminate background sound. Hence, it is hard to hear clearly. Other than that, for a worker who is working in a high noise environment, repeated exposure to loud noise can lead to severe hearing degradation or permanent hearing loss. There are four proposed filters used for this paper, and four proposed adaptive algorithms in the Assistive Listening System (ALS). These filters and adaptive algorithms are significant to investigate the preprocessing to achieve a high level of Signal- to-Noise Ratio (SNR) performance with a low noise level. Preprocessing method is crucial to minimize the background noises and to eliminate the risk of corrupt hearing. After the findings, the best filter used for preprocessing is the Butterworth Lowpass Filter with a maximum output power of 65 dB; and the best adaptive algorithm is Smart Noise Canceller with an output SNR of 80.43 dB.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Industries, Noise reduction, Adaptive algorithms, Auditory system, Filtering algorithms, Noise cancellation, Intelligent systems
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Electrical and Electronic Engineering Technology Institute of Postgraduate Studies
Depositing User:	Dr. Roshahliza M Ramli
Date Deposited:	21 Apr 2022 02:59
Last Modified:	21 Apr 2022 02:59
URI:	http://umpir.ump.edu.my/id/eprint/33739
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